Attractor and Boundedness of Switched Stochastic Cohen-Grossberg Neural Networks

Chuangxia Huang, Jie Cao and Peng Wang

Discrete Dynamics in Nature and Society, 2016, vol. 2016, 1-19

Abstract:

We address the problem of stochastic attractor and boundedness of a class of switched Cohen-Grossberg neural networks (CGNN) with discrete and infinitely distributed delays. With the help of stochastic analysis technology, the Lyapunov-Krasovskii functional method, linear matrix inequalities technique (LMI), and the average dwell time approach (ADT), some novel sufficient conditions regarding the issues of mean-square uniformly ultimate boundedness, the existence of a stochastic attractor, and the mean-square exponential stability for the switched Cohen-Grossberg neural networks are established. Finally, illustrative examples and their simulations are provided to illustrate the effectiveness of the proposed results.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnddns:4958217

DOI: 10.1155/2016/4958217

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